VizProg: Identifying Misunderstandings by Visualizing Students' Coding Progress

要旨

Programming instructors often conduct in-class exercises to help them identify students that are falling behind and surface students' misconceptions. However, as we found in interviews with programming instructors, monitoring students' progress during exercises is difficult, particularly for large classes. We present VizProg, a system that allows instructors to monitor and inspect students' coding progress in real-time during in-class exercises. VizProg represents students' statuses as a 2D Euclidean spatial map that encodes the students' problem-solving approaches and progress in real-time. VizProg allows instructors to navigate the temporal and structural evolution of students' code, understand relationships between code, and determine when to provide feedback. A comparison experiment showed that VizProg helped to identify more students' problems than a baseline system. VizProg also provides richer and more comprehensive information for identifying important student behavior. By managing students' activities at scale, this work presents a new paradigm for improving the quality of live learning.

受賞
Honorable Mention
著者
Ashley Ge. Zhang
University of Michigan, Ann Arbor, Ann Arbor, Michigan, United States
Yan Chen
University of Toronto, Toronto, Ontario, Canada
Steve Oney
University of Michigan, Ann Arbor, Michigan, United States
論文URL

https://doi.org/10.1145/3544548.3581516

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Programming

Room Y01+Y02
6 件の発表
2023-04-25 01:35:00
2023-04-25 03:00:00